Querying Heterogeneous Data for Non-Expert Users

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Using the Internet to answer our questions is an almost daily practice, typically, using search engines like Google or Bing. Usually, this task involves browsing web pages to find the answer. In the past few years, if the question is simple (e.g., finding a fact about a famous entity), the answer is shown in what is called an infobox. Search engines show this fact by accurately extracting the entity of interest in the question written in natural language and map it to its equivalent entity in their internal data representation of the world. This internal representation is of course proprietary and different from one search engine to another. However, the approach is the same: The data is stored in a structured format (tables or what is typically called a knowledge graph) and queries are issued against it. Natural language is one of the most popular interfaces to access the underlying structured data. The simplicity of this approach allows non-expert users to access this plethora of datasets. However, retrieving accurate answers to natural language queries is a challenging task for even slightly more complicated questions. In this scenario, in order to retrieve accurate answers, the user needs to write a formal query in a structured query language. Of course, such a requirement is not practical and limits harnessing the power of knowledge available in the underlying structured data to power users who have sufficient technical skills to interact with the complex datasets. This research program aims at helping non-expert users to query datasets that come from heterogeneous sources and are stored in different formats. To achieve this goal, we will work on how to integrate data from multiple sources and formats for it to be ready for querying, how to build a querying system that supports natural language interfaces and other novel querying models to help a non-expert user to query the integrated data, and finally how to build benchmarks that can accurately assess the quality of the querying system.